Comprehensive Benchmark Analysis of Convolutional and Transformer-based Models for Medical Image Classification

This work presents a comprehensive benchmark analysis of convolutional and Transformer-based models for medical image classification across diverse datasets, training schemes, and input resolutions. The findings challenge prevailing assumptions regarding model design, training schemes, and input resolution requirements, and provide insights to inform the development of more efficient and effective models.